System and method for optimizing utility pipe sensors placement using artificial intelligence technology

ABSTRACT

A computer-implemented method and system for determining placement of a sensor component on a utility pipe. Data relating to the utility pipe is inputted which is processed to generate one or more variables. One or more models are trained, via the one or more variables, to produce an output indicative of a likelihood of failure variable associated with the utility pipe from each model. The outputs from all models are preferably combined into an ensemble output indicative of a likelihood of failure associated with the utility pipe. A consequence of failure variable associated with the utility pipe is determined preferably utilizing a plurality of weighted variables. A sensor placement determinative variable is then determined contingent upon the ensemble output and the consequence of failure variable associated with the utility pipe. Feedback data is then provided indicative of physical placement of one or more sensor components associated with the utility pipe based at least in part on the sensor placement determinative variable.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.62/913,671 filed Oct. 10, 2019 which is incorporated herein by referencein its entirety.

FIELD OF THE INVENTION

The disclosed embodiments generally relate to utility pipe leakdetection technology, and more particularly to a system and method forprobabilistically determining using machine learning techniques theoccurrence of leaks in utility pipes for determining leak sensorplacement.

BACKGROUND OF THE INVENTION

Drinking water is a scarce resource, yet much of it is lost through pipefailures during its journey to consumers. The World Bank estimates thatwater leaks cost utilities up to $14 billion per year globally. In theU.S. alone, there are more than 240,000 water leaks per year, a loss ofnearly one out of seven gallons of treated water. Compounding theproblem is an aging pipe infrastructure, with many pipes reaching theend of their useful life within the next thirty years.

Water pipe leaks range from small and steady to large and catastrophic.Furthermore, some leaks surface above ground causing visible water lossand/or ground changes (e.g. sinkholes) whereas other leaks remainunderground and can be hidden for extended periods providing no visibleleak indication. The visible leaks are easy to find and are oftenreported directly by the public, whereas the hidden leaks can remainunknown to the water utilities and may be leaking for an extended periodof time. These hidden leaks are often referred to by the water industryas “Non-Revenue Water” (NRW), and represent a significant problembecause of lost revenue and the negative environmental impact caused byhidden leaks. It is to be appreciated that NRW levels vary from countryto country. However, even in countries with advanced infrastructure, NRWlevels can reach significant levels. For instance, the United Kingdom'sNRW represents 19% of its total water produced, France 26%, and Italy29%.

It is noted that prior art techniques have been devised for identifyinghidden water leaks. These techniques typically place sensors in variouslocations within the water network infrastructure of a municipality tocollect data which is then processed and analyzed to identify possiblyleaks. For example, Echologics LLC has developed a proprietarytechnology that uses acoustic sensors placed in water hydrants tocollect audio signals which are analyzed to identify and locate thesound of leaking water. These methods can have acceptable accuracy,however they are expensive and infeasible to deploy across a completewater pipe infrastructure. As a consequence, water utilities selectivelypick only a few locations within their network to monitor using thesesensor-based technologies.

There is thus a need to determine optimal locations for deploying leaksensors for detecting hidden leaks. Often picking these locations issubjective and based on the opinion of human experts, which is expensiveand often non-effective for determining optimal locations forpositioning leak sensors.

SUMMARY OF THE INVENTION

The purpose and advantages of the below described illustratedembodiments will be set forth in and apparent from the description thatfollows. Additional advantages of the illustrated embodiments will berealized and attained by the devices, systems and methods particularlypointed out in the written description and claims hereof, as well asfrom the appended drawings.

In furtherance of the illustrated embodiments discussed herein, it is tobe appreciated and understood Artificial Intelligence (AI) may be usedto support decision making determinations associated with water pipeleaks by providing processes which calculate the probability of anywater pipe failing that occurs in a future time period. Thus, and inaccordance with the below described illustrated embodiments, varioussensors may be deployed strategically to monitor and examine water pipesthat represent a highest Risk within the water infrastructure, whereRisk is to be understood to be defined as a function of Likelihood ofFailure (LoF) and Consequence of Failure (CoF) associated with the waterpipe. This Risk assumption is predicated upon that pipes with high LoFmay have already failed causing a leak that hasn't surfaced yet but canbe detected by the appropriate equipment, or they will fail soon andthus need to be monitored closely.

To achieve these and other advantages and in accordance with the purposeof the illustrated embodiments, in one aspect, a computer-implementedmethod and system for determining placement of a sensor component on autility pipe is described in which at one or more computing devices,data relating to the utility pipe is inputted. The inputted data is thenprocessed to generate one or more variables. The one or more computerdevices, via the one or more variables, preferably trains one or moremodels to produce an output indicative of a likelihood of failurevariable associated with the utility pipe from each model. The outputsfrom all models are preferably combined into an ensemble outputindicative of a likelihood of failure associated with the utility pipe.A consequence of failure variable associated with the utility pipe isdetermined preferably utilizing a plurality of weighted variables. Asensor placement determinative variable is then calculated contingentupon the ensemble output and the consequence of failure variableassociated with the utility pipe. Feedback data is then providedindicative of physical placement of one or more sensor componentsassociated with the utility pipe based at least in part on the sensorplacement determinative variable.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying appendices and/or drawings illustrate variousnon-limiting, example, inventive aspects in accordance with the presentdisclosure:

FIG. 1 illustrates an exemplary system overview and data-flow for usewith an illustrated embodiment for depicting system operation;

FIG. 2 illustrates an example user computing device configured inaccordance with the illustrated embodiments; and

FIGS. 3-5 illustrate flow processes in accordance with the illustratedembodiments.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The illustrated embodiments are now described more fully with referenceto the accompanying drawings wherein like reference numerals identifysimilar structural/functional features. The illustrated embodiments arenot limited in any way to what is illustrated as the illustratedembodiments described below are merely exemplary, which can be embodiedin various forms, as appreciated by one skilled in the art. Therefore,it is to be understood that any structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as abasis for the claims and as a representation for teaching one skilled inthe art to variously employ the discussed embodiments. Furthermore, theterms and phrases used herein are not intended to be limiting but ratherto provide an understandable description of the illustrated embodiments.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the illustrated embodiments,exemplary methods and materials are now described.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an,” and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “astimulus” includes a plurality of such stimuli and reference to “thesignal” includes reference to one or more signals and equivalentsthereof known to those skilled in the art, and so forth.

It is to be appreciated the illustrated embodiments discussed below arepreferably a software algorithm, program or code residing on computeruseable medium having control logic for enabling execution on a machinehaving a computer processor. The machine typically includes memorystorage configured to provide output from execution of the computeralgorithm or program.

As used herein, the term “software” is meant to be synonymous with anycode or program that can be in a processor of a host computer,regardless of whether the implementation is in hardware, firmware or asa software computer product available on a disc, a memory storagedevice, or for download from a remote machine. The embodiments describedherein include such software to implement the equations, relationshipsand algorithms described above. One skilled in the art will appreciatefurther features and advantages of the illustrated embodiments based onthe above-described embodiments. Accordingly, the illustratedembodiments are not to be limited by what has been particularly shownand described, except as indicated by the appended claims.

Turning now descriptively to the drawings, in which similar referencecharacters denote similar elements throughout the several views, FIG. 1depicts an exemplary communications network 100 in which belowillustrated embodiments may be implemented.

It is to be understood a communication network 100 is a geographicallydistributed collection of nodes interconnected by communication linksand segments for transporting data between end nodes, such as personalcomputers, workstations, smart phone devices, tablets, televisions,sensors and or other devices such as automobiles, etc. Many types ofnetworks are available, with the types ranging from local area networks(LANs) to wide area networks (WANs). LANs typically connect the nodesover dedicated private communications links located in the same generalphysical location, such as a building or campus. WANs, on the otherhand, typically connect geographically dispersed nodes overlong-distance communications links, such as common carrier telephonelines, optical lightpaths, synchronous optical networks (SONET),synchronous digital hierarchy (SDH) links, or Powerline Communications(PLC), and others.

FIG. 1 is a schematic block diagram of an example communication network100 illustratively comprising nodes/ user devices 101-108 (e.g., sensors102, client computing devices 103, smart phone devices 105, web servers106, routers 107, switches 108, and the like) interconnected by variousmethods of communication. For instance, the links 109 may be wired linksor may comprise a wireless communication medium, where certain nodes arein communication with other nodes, e.g., based on distance, signalstrength, current operational status, location, etc. Moreover, each ofthe devices can communicate data packets (or frames) 142 with otherdevices using predefined network communication protocols as will beappreciated by those skilled in the art, such as various wired protocolsand wireless protocols etc., where appropriate. In this context, aprotocol consists of a set of rules defining how the nodes interact witheach other. Those skilled in the art will understand that any number ofnodes, devices, links, etc. may be used in the computer network, andthat the view shown herein is for simplicity. Also, while theembodiments are shown herein with reference to a general network cloud,the description herein is not so limited, and may be applied to networksthat are hardwired.

As will be appreciated by one skilled in the art, aspects of theillustrated embodiments may be embodied as a system, method or computerprogram product. Accordingly, aspects of the illustrated embodiments maytake the form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the illustrated embodiments may take the form ofa computer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium (e.g., such as an “app” downloadable from an app store (e.g.,iTunes™)) or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, cloud service or any suitable combination of theforegoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, an orany suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of theillustrated embodiments may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the illustrated embodiments are described below withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the illustrated embodiments. It will be understood thateach block of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a processor of aspecial purpose computer, or other programmable data processingapparatus to produce a machine, such that the instructions, whichexecute via the processor of the computer or other programmable dataprocessing apparatus, create means for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

With reference now to FIG. 2, shown is a schematic block diagram of anexample network user computing device 200 (e.g., a smart phone 105,etc.) that may be used (or components thereof) with one or moreillustrated embodiments described herein. As explained above, indifferent embodiments these various devices are configured tocommunicate with each other in any suitable way, such as, for example,via communication network 100.

Device 200 is intended to represent any type of user computer systemcapable of carrying out the teachings of various embodiments of theillustrated embodiments. Device 200 is only one example of a suitablesystem and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the illustrated embodimentsdescribed herein. Regardless, user device 200 is capable of beingimplemented and/or performing any of the functionality set forth herein.

User device 200 is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with computing device 200include, but are not limited to, tablet devices and preferably otherportable user computing devices (e.g., hand-held or laptop devices) thatinclude any of the above systems or devices, and the like.

User device 200 may be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.User device 200 may be practiced in distributed data processingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed dataprocessing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

User device 200 is shown in FIG. 2 in the form of a user computingdevice. The components of device 200 may include, but are not limitedto, one or more processors or processing units 216, a system memory 228,and a bus 218 that couples various system components including systemmemory 228 to processor 216 and one or more camera components.

Bus 218 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

User device 200 typically includes a variety of computer system readablemedia. Such media may be any available media that is accessible bydevice 200, and it includes both volatile and non-volatile media,removable and non-removable media.

System memory 228 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 230 and/or cachememory 232. Computing device 200 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 234 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). As will be furtherdepicted and described below, memory 228 may include at least oneprogram product having a set (e.g., at least one) of program modulesthat are configured to carry out the functions of embodiments of theinvention.

Program/utility 240, having a set (at least one) of program modules 215,such as underwriting module, may be stored in memory 228 by way ofexample, and not limitation, as well as an operating system, one or moreapplication programs, other program modules, and program data. Each ofthe operating system, one or more application programs, other programmodules, and program data or some combination thereof, may include animplementation of a networking environment. Program modules 215generally carry out the functions and/or methodologies of embodiments ofthe illustrated embodiments as described herein.

Device 200 may also communicate with one or more external devices 214such as a keyboard, a pointing device, one or more camera components, adisplay 224, etc.; one or more devices that enable a user to interactwith computing device 200; and/or any devices (e.g., network card,modem, etc.) that enable computing device 200 to communicate with one ormore other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 222. Still yet, device 200 can communicatewith one or more networks such as cellular networks (e.g., TDMA, CDMA, 4g and 5 g); a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter220. As depicted, network adapter 220 communicates with the othercomponents of computing device 200 via bus 218. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with device 200. Examples, include, but are notlimited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

FIGS. 1 and 2 are intended to provide a brief, general description of anillustrative and/or suitable exemplary environment in which embodimentsof the below described illustrated embodiments may be implemented. FIGS.1 and 2 are exemplary of a suitable environment and are not intended tosuggest any limitation as to the structure, scope of use, orfunctionality of an embodiment of the illustrated embodiments. Aparticular environment should not be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated in an exemplary operating environment. Forexample, in certain instances, one or more elements of an environmentmay be deemed not necessary and omitted. In other instances, one or moreother elements may be deemed necessary and added.

With the exemplary communication network 100 (FIG. 1) and user device200 (FIG. 2) being generally shown and discussed above, description ofcertain illustrated embodiments of the present invention will now beprovided. As will be further appreciated from the below discussion ofthe certain illustrated embodiments, pipe-related data is accessedpreferably from a water utility (e.g. pipe age, material, size,pressure, volume, leak/break history, etc.) and is preferably combinedwith publicly accessible data regarding environmental conditions thepipe is subjected to (e.g. temperature changes, rainfall, soil moistureand type, seismic activity, elevation, traffic flow, etc.) to derivedeterminative probabilistic data regarding sensor placement for asubject water utility pipe. Preferably, machine learning algorithms areutilized for detecting and learning patterns that preceded previous pipebreaks to generate a probability score (e g , Likelihood of Failure(LoF)) for each utility pipe failing in a future time period (e.g.,days, weeks, months, years). The LoF score for every pipe is preferablycombined with its determined Consequence of Failure (CoF) score (whichmay preferably be a function of replacement costs, environmental impact,affected population size, potential traffic disruptions, etc) todetermine a final risk metric value. Contingent upon the determined riskmetric value, hardware sensors may then be positioned in optimumlocations based on the distribution of risk scores within the pipenetwork.

It is to be appreciated that the subject invention is applicable todetermining leaks in utility pipes, and for ease of description,discussion is provided with regards to a water utility pipe. However,the illustrated embodiments are not to be understood to be limited towater utility pipe application as other fluid and gas carrying waterpipes and systems are to be understood to be encompassed by theillustrated embodiments.

In accordance with the illustrated embodiments, data relating to autility pipe, such as a water pipe, is accessed and preferably cleansedfrom a utility company regarding their pipes and breaks. This obtaineddata typically includes information regarding pipe networks, such astopology of the water pipes, installation year for each pipe, material,diameter, length, etc. In addition, utility companies may also provideknown pipe break history including dates for certain pipe breaks,location, cause of break (if known), etc. However, it is known that suchaccessed data often contains errors and omissions attributable tovarious reasons, such as: errors introduced when legacy paper recordswere converted to digital format, data entry errors, crews that repairpipe breaks recorded erroneous location of breaks, and the like. Toobviate these occurrences, statistical techniques are utilized in theillustrated embodiments to correct such errors. For instance, outliersare detected and replaced by statistically valid values, or may beremoved from a dataset.

Additionally, publicly available data related to the geographic locationof the pipe network is collected, which may include for instance soildata obtained from the USGS databases, weather data obtained from NOAA,population density obtained from the Census Bureau relating to theenvironment and/or geography associated with a subject water pipe. Thispublic data is then preferably combined with the aforesaid data accessedfrom a utility company to enrich a dataset regarding a subject waterpipe. For instance, accumulated data relating to a subject water piperecord may include the type of soil it is surrounded by, the populationsize it serves, and the weather conditions present during prior breaks.It is to be appreciated this accumulated data is beneficial during amachine learning phase when a model is trained to identify theconditions under which a water pipe is most likely to leak or break.

It is to be appreciated that the aforesaid obtained data variables arecombined to create new data variables utilized to calculate aprobability of failure for a water pipe. For example, a created datavariable may combine geometric features of a pipe with populationdensity and soil data the water pipe is associated with. These createddata variables may also include data transformations enabling the waterpipe data to be better suited for particular machine learningalgorithms. For instance, a linear regression algorithm works well whenindependent variables have linear relationships with the dependentvariables, whereby if the actual relationship of a variable isexponential, then a new variable may be created using the logarithmicfunction, such that the resulting relationship can be transformed tolinear.

Once the aforesaid data preparation is completed, the resulting datasetis preferably utilized to train several machine learning modelsrepresenting various algorithms, such as: Logistic Regression; SupportVector Machines; Neural Networks; Bayesian Networks and the like. It isto be understood that each machine learning model produces a differentset of outputs. Hence, in order to obtain an optimal result, thedifferent outputs from the models are preferably combined through anEnsemble technique, such as voting or a weight-combination. The outputof the Ensemble technique is a Likelihood of Failure (LoF) probabilityfor every pipe in the system of a utility company that data was providedfor.

A Consequence of Failure (CoF) variable is then calculated for each suchpipe in the pipe network. It is to be appreciated the calculation of CoFvariables is contingent upon various factors related to the consequencesof failure of a certain pipe, such as cost of pipe repair, cost of pipereplacement, potential environmental damage, size of affectedpopulation, important facilities such as hospitals or schools servicedby the pipe, potential traffic disruptions caused by failure of the pipeand the like. A numeric score is assigned to each one of these factorsassociated with the pipe, and these individual scores are combined witheach other, preferably using weighted values, to determine a final CoFmetric. The overall Risk metric for a water pipe is calculated as afunction of LoF and CoF, such as (but not to be limited to):Risk=LoF*CoF.

Based on the aforesaid calculated Risk metric for each water pipe in apipe network, optimal locations for placing pipe leak sensors are thencalculated. An exemplary algorithm for calculating such locations iscontingent on the type and number of leak sensors, as well as beingcontingent upon any restrictions associated with their use. Forinstance, if the sensors are acoustic and require attachment to waterhydrants, then the location of the hydrants is necessary for thecalculations of optimal sensor placements. That is, each hydrant may beassigned a score based on the cumulative Risk of the water pipes thatcan be monitored through that hydrant, and thus the hydrants with thehighest score are preferably given priority for sensor placement. Thus,the potential locations for placing sensors are ranked and sorted bytheir determined probabilistic effectiveness in catching the most likelyand most catastrophic water leaks.

With particular reference now to FIGS. 3-5 of the certain illustratedembodiments, and commencing at process 300 of FIG. 3, starting at step310, data is inputted into the one or more computer devices 200, whichpreferably includes data provided by (and/or electronically accessedfrom) utility companies preferably including information associated withtheir respective pipe networks. For instance, such accessed/provideddata may consist of (but is not to be limited to) the topologyassociated with a utility pipe, installation year for each utility pipe,material of each utility pipe, diameter of each utility pipe, length ofeach utility pipe and other data regarding certain parameters of utilitypipes provided and/or serviced by the subject utility company. Datainputted at step 310 may also include information regarding known breakhistory for each utility pipe break. In accordance with the illustratedembodiments, the data inputted at step 310 may be subjected toperformance of statistical cleansing techniques by the one or morecomputer devices 200 for correcting errors and omissions detected in theinputted data (e.g., conversion errors and data entry errors, as notedabove). Additionally in accordance with the illustrated embodiments, thedata inputted at step 310 may be further subjected to normalizingdetected errors in the data by detecting outliers which may be replacedwith statistically valid values, or the detected outliers be entirelyremoved from the data.

It is to further understood that the data inputted at step 310 mayfurther include data provided by, or accessed from, public sourcesregarding geographic public data associated with subject utility pipes,which for example, may include soil data obtained from the USGSdatabases, weather data obtained from NOAA, and population densityobtained from the Census Bureau. Additional examples of geographicpublic provided/accessed data in step 310 may include utility piperecords, soil type surrounding utility pipes, size of the populationserviced by the utility pipe, and weather conditions during priorutility pipe breaks. Additionally, the data inputted at step 310 mayalso consist of a combination of geographic public data combined withexisting utility pipe data for enriching the aggregated inputted datasetwhich consequently preferably enhances performance of process 300 in theone or more devices 200.

Next, at step 320, the inputted data (step 310) is processed to generateone or more variables at the one or more computer devices 200.Preferably, this processing includes combining inputted data 310 tocreate proprietary variables that can be useful in calculating alikelihood of failure (LoF) for each utility pipe. Processing 320 theinputted data 310 may also include generating data transformations foruse in training of the one or more models and/or executing thecalculation of the LoF variable for each utility pipe. For instance, theLoF variable of a subject utility pipe is preferably generated basedupon the combination of geometric features of a utility pipe withgeographic population density and soil data obtained from the inputteddata of step 310. For instance, in the processing by the one or moredevices 200 of step 320, the aforesaid data transformations aregenerated using one or more of (and is not to be understood to belimited to): linear regression algorithms, statistical error-correcting,statistical data filling techniques, and data augmentation methods suchas Synthetic Minority Over-Sampling Techniques (SMOTE) or GenerativeAdversarial Networks (GANs) for optimizing the inputted data (step 310)for accommodation by individual machine-learning methods. For example,in step 320, a linear regression algorithm may perform well when anindependent variable has linear relationships with a dependent variablesuch that if the actual relationship of a variable is exponential, a newvariable is then created using the logarithmic function such that thatthe resulting relationship is transformed to linear.

Proceeding the step 330 of process 300, the aforementioned training ofthe one or more models preferably produces an output indicative of a LoFvariable associated with the subject utility pipe from each model at theone or more computer devices 200, via the one or more variables. It isto be understood a resulting dataset is produced from the inputted data(step 310) and training process (step 330). During the training process400 (step 330), as detailed in FIG. 4, the process 400 begins with theinput of data from the resulting data set (step 410), which is thenpreferably subjected to one-hot encoding (step 420). It is to beappreciated the input data (step 410) may be separated into training andvalidation sets (step 430), which is preferably utilized in training andtuning models (step 440) during the training process (step 330), orduring another process stage. For instance, examples of training (step330) on the one or more models to produce one or more individual outputsduring process 400 may include utilizing Machine Learning techniques,such as (but not to be understood to be limited to): SupervisedLearning, Unsupervised Learning, Transfer Learning, ReinforcementLearning, Clustering, Classification, Dimensionality Reduction, EnsembleMethods, and/or Deep Learning. It is to be understood the training (step330) of the one or more models may include the one or more models eachproducing an individual output.

Proceeding to step 340 of process 300, the outputs from the models areutilized to preferably produce an ensemble output indicative of a LoFvariable associated with a subject utility pipe. With reference to FIG.4, in step 450 combining the outputs (step 340) preferably includescreating an ensemble output, which is then executes (step 460) togenerate an ensemble output as a collective LoF variable per utilitypipe (step 470). For example, combining the outputs (step 340) from themodels may preferably occur through an ensemble technique, such asvoting or a weight-combination. The result of combining the outputs 340may be the generation of a LoF variable for every utility pipe in autility pipe network.

Proceeding now to step 350 of process 300, a Consequence of Failure(CoF) variable is now calculated that is associated with the subjectutility pipe. In accordance with the illustrated embodiments, the CoFvariable is calculated preferably utilizing a plurality of weightedvariables at the one or more computer devices 200. It is to beunderstood that calculating the CoF variable (step 350) may also becalculated in regard to each subject utility pipe in a utility pipenetwork for which data is accessed/provided (step 310). With referenceto process 500 of FIG. 5, the calculation of the CoF variable occurs viaprocess 500, which preferably is contingent upon weighted variablesrelated to the CoF variable. For instance, examples of such weightedvariables may include environmental impact 510, cost factors 520,proximity to points of interest 530, road type 540, population affected550, and areas of significance 560, each being associated with thesubject utility pipe(s). In step 570 of process 500 performed by the oneor more devices 200, a numeric score is preferably assigned to each oneof these weighted variables, which variables are then combined with eachother through a customer-specific set of weights to result incalculating a CoF variable (step 350, FIG. 3 and step 580, FIG. 5).

In accordance with the illustrated embodiments, at step 360 of process300 a sensor placement determinative variable is determined by the oneor more devices 200, preferably via the ensemble output (LoF variable)(step 470) and the determined CoF variable (step 580) each associatedwith a subject utility pipe. It is to be appreciated the calculatedsensor placement determinative variable (step 360) is preferablygenerated as a function of the LoF variable (step 470) and the CoFvariable (step 580), which in one illustrated embodiment may becalculated by the equation:

sensor placement determinative variable=LoF variable*CoF variable

However, it is to be understood and appreciated that the illustratedembodiments may encompass other calculation methodologies fordetermining a sensor placement determinative variable (step 360).

Next, at step 370 of process 300, the one or more devices 200 areconfigured to provide feedback output data that is indicative ofphysical placement of one or more sensor components associated with thesubject utility pipe based at least in part on the aforesaid sensorplacement determinative variable (step 360). It is to be understood thatproviding feedback output data (step 370) preferably indicates anoptimum location for placing one or more sensor components for detectingleaks/breaks associated with the subject utility pipe. It is to befurther understood that providing feedback output data (step 370) may becontingent upon a specific geographic location of the subject utilitypipe, and may further depend on the type and number of the one or moresensor components to be associated with the subject utility pipe, whichmay include restrictions associated with their deployment or usage. Forinstance, if the one or more sensor components associated with thesubject utility pipe are acoustic and are required to be attached toassociated water hydrants, then the location of the hydrants may benecessary for the calculation of the optimum sensor physical placementsas part of providing feedback output data (step 370) based at least inpart on the sensor placement determinative variable (step 360). Forexample, a certain hydrant may be assigned a score value dependent uponthe cumulative weight of the sensor placement determinative variable(step 360) regarding the utility pipes which may be monitored throughthat specific hydrant, via the feedback output data (step 370).Therefore, the hydrants with the most favorable score may be givenpriority for the placement of the one or more sensor components. It isthus to be appreciated that by providing this feedback output data (step370), the ranking of potential locations for the placement of the one ormore sensor components is generated, which may preferably be sorted bytheir effectiveness in detecting likely and catastrophic leaksassociated with the subject utility pipe.

With certain illustrated embodiments described above, it is to beappreciated that various non-limiting embodiments described herein maybe used separately, combined or selectively combined for specificapplications. For instance, the one or more user devices 200 may beconfigured to enable a policyholder user to record one or more personalproperty losses associated with the structure. Further, some of thevarious features of the above non-limiting embodiments may be usedwithout the corresponding use of other described features. The foregoingdescription should therefore be considered as merely illustrative of theprinciples, teachings and exemplary embodiments of this invention, andnot in limitation thereof. For instance, the attached Appendix containsfurther illustrative features of the illustrated embodiments.

It is to be understood that the above-described arrangements are onlyillustrative of the application of the principles of the illustratedembodiments. Numerous modifications and alternative arrangements may bedevised by those skilled in the art without departing from the scope ofthe illustrated embodiments, and the appended claims are intended tocover such modifications and arrangements.

What is claimed is:
 1. A computer-implemented method for determiningplacement of a sensor component on a utility pipe, comprising:inputting, at one or more computing devices, data relating to theutility pipe; processing, at the one or more computer devices, theinputted data to generate one or more variables; training, at the one ormore computer devices via the one or more variables, one or more modelsto produce an output indicative of a likelihood of failure variableassociated with the utility pipe from each model; combining the outputsfrom all models into an ensemble output indicative of a likelihood offailure associated with the utility pipe; calculating, at the one ormore computer devices, a consequence of failure variable associated withthe utility pipe utilizing a plurality of weighted variables;calculating, at the one or more computer devices via the ensemble outputand the consequence of failure variable associated with the utilitypipe, a sensor placement determinative variable; and providing feedback,from the one or more computer devices, output data indicative ofphysical placement of one or more sensor components associated with theutility pipe based at least in part on the sensor placementdeterminative variable.
 2. The computer-implemented method of claim 1,wherein the inputted data includes information accessed from a utilitycompany associated with the utility pipe.
 3. The computer-implementedmethod of claim 1, wherein the inputted data includes geographicenvironmental data associated with a geographic environment the utilitypipe is exposed to.
 4. The computer-implemented method of claim 1,further including, detecting, in the one or more computer devices,errors and omissions in the inputted data and correcting the detectederrors and omissions in the inputted data using computer-implementedstatistical techniques.
 5. The computer-implemented method of claim 1,wherein processing the inputted data further includes generating datatransformations for use in training of the one or more models orexecuting the calculation of the likelihood of failure variable.
 6. Thecomputer-implemented method of claim 1, wherein the data transformationsare generated using linear regression algorithms, statisticalerror-correcting, statistical data filling techniques, and dataaugmentation methods such as Synthetic Minority Over-Sampling Techniques(SMOTE) or Generative Adversarial Networks (GANs) for optimizing theinputted data for individual machine-learning methods.
 7. Thecomputer-implemented method of claim 1, wherein training the one or moremodels includes the one or more models each producing an individualoutput.
 8. The computer-implemented method as recited in claim 7,wherein training the one or more models to produce one or moreindividual outputs includes utilizing at least one Machine Learningtechnology such as Supervised Learning, Unsupervised Learning, TransferLearning, Reinforcement Learning, Clustering, Classification,Dimensionality Reduction, Ensemble Methods, or Deep Learning.
 9. Thecomputer-implemented method of claim 1, wherein the ensemble output isdefined as a collective likelihood of failure variable.
 10. Thecomputer-implemented method of claim 1, wherein the utility pipe is awater pipe.
 11. A computer system for determining placement of a sensorcomponent on a utility pipe, comprising: a memory configured to storeinstructions; a processor disposed in communication with said memory,wherein said processor upon execution of the instructions is configuredto: input data relating to the utility pipe; detect errors and omissionsin the input data and correct the detected errors and omissions in theinput data using computer-implemented statistical techniques; processthe inputted data to generate one or more variables; train, via the oneor more variables, one or more models to produce an output indicative ofa likelihood of failure variable associated with the utility pipe;combine the outputs from all models into an ensemble output indicativeof a likelihood of failure associated with the utility pipe; calculate aconsequence of failure variable associated with the water utility pipeutilizing a plurality of weighted variables; calculate, via the ensembleoutput and the consequence of failure variable associated with theutility pipe, a sensor placement determinative variable; and providefeedback, from the one or more computer devices, output data indicativeof physical placement of one or more sensor components associated withthe utility pipe based at least in part on the sensor placementdeterminative variable.
 12. The computer system as recited in claim 11,wherein the inputted data includes information accessed from a utilitycompany associated with the utility pipe and geographic environmentaldata associated with a geographic environment the utility pipe isexposed to.
 13. The computer system of claim 11, wherein processing theinput data further includes generating data transformations for use intraining of the one or more models and executing the calculation of thelikelihood of failure variable.
 14. The computer system of claim 11,wherein the data transformations are generated using linear regressionalgorithms, statistical error-correcting, statistical data fillingtechniques, and data augmentation methods such as Synthetic MinorityOver-Sampling Techniques (SMOTE) or Generative Adversarial Networks(GANs) for optimizing the inputted data for individual machine-learningmethods.
 15. The computer system as recited in claim 11, whereintraining the one or more models includes the one or more models eachproducing an individual output.
 16. The computer system as recited inclaim 15, wherein training the one or more models to produce one or moreindividual outputs includes utilizing at least one Machine Learningtechnology such as Supervised Learning, Unsupervised Learning, TransferLearning, Reinforcement Learning, Clustering, Classification,Dimensionality Reduction, Ensemble Methods, or Deep Learning.
 17. Thecomputer system as recited claim 11, wherein the ensemble output isdefined as a collective likelihood of failure variable.
 18. The computersystem as recited in claim 1, wherein the utility pipe is a water pipe.19. A non-transitory computer readable storage medium and one or morecomputer programs embedded therein, the computer programs comprisinginstructions, which when executed by a computer system, cause thecomputer system to: input data relating to a utility water pipeincluding information accessed from a utility company associated withthe utility water pipe and geographic environmental data associated witha geographic environment the utility water pipe is exposed to; detecterrors and omissions in the input data and correct the detected errorsand omissions in the input data using computer-implemented statisticaltechniques; process the inputted data to generate one or more variablesand generate data transformations for use in training of the one or moremodels wherein the data transformations are generated optimizing theinputted data for individual machine-learning methods; train, via theone or more variables, one or more models to produce an outputindicative of a likelihood of failure variable associated with theutility pipe; combine the outputs from all models into an ensembleoutput indicative of a likelihood of failure associated with the utilitypipe; calculate a consequence of failure variable associated with thewater utility pipe utilizing a plurality of user weighted variables;calculate, via the ensemble output and the consequence of failurevariable associated with the utility pipe, a sensor placementdeterminative variable; and provide feedback, from the one or morecomputer devices, output data indicative of physical placement of one ormore sensor components associated with the utility pipe based at leastin part on the sensor placement determinative variable.
 20. Thenon-transitory computer readable storage medium, as recited in claim 19,wherein training the one or more models includes the one or more modelseach producing an individual output and wherein training the one or moremodels to produce one or more individual outputs includes utilizing atleast one Machine Learning technology such as Supervised Learning,Unsupervised Learning, Transfer Learning, Reinforcement Learning,Clustering, Classification, Dimensionality Reduction, Ensemble Methods,or Deep Learning.